Accounting for Sea Surface Wind Variability in the Ocean Calibration of Ocean Surface Current Airborne Radar (OSCAR)

Airborne flight campaigns are essential to evaluate the performance of new ocean remote sensing radars before launch. However, the accurate calibration of airborne radars is quite challenging due to the limited amount of experimental data and the impact of small-scale wind variability. For example, during the flight experiments of the ocean surface current airborne radar (OSCAR), the conventional ocean calibration (OC) shows up to 4-dB difference between the calibration curves from different flight legs. One possible reason for such inconsistency is small-scale wind variability captured by OSCAR but not by the reference winds. As such, this article proposes a new OC method to effectively address both the first-order backscattering bias and the inherent wind variability simultaneously. The calibration technique and wind retrieval are integrated into an iterative framework, thus ensuring that the calibration results are converging and self-consistent. Then, verification against spaceborne scatterometer winds proves that the proposed calibration strategy generally leads to higher quality leg-mean wind retrievals than the conventional OC. Finally, the feasibility of retrieving high spatial resolution wind data from OSCAR test flights is evaluated. As expected, OSCAR can capture finer small-scale wind variations compared to both numerical models and scatterometers, thus offering high-quality wind data for fine-scale research and applications.